Use of multi-beam sonar to map seagrass beds in Otsuchi Bay on the Sanriku Coast of Japan

Komatsu, Toshimitsu; Igarashi, Chiaki; Tatsukawa, Kenichi; Sultana, Sayeeda; Matsuoka, Yasuaki; Harada, Shuichi

doi:10.1016/s0990-7440(03)00045-7

Cited by 87 publications

(38 citation statements)

References 17 publications

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“…In order to validate and evaluate the two classification approaches, groundtruth information collected by diving prospection and independent echo-sounder data derived from the Furuno System were examined. This study links different mapping approaches which act on different scales such as optical remote sensing in intertidal areas (Hennig et al, 2007;Oppelt et al, 2012) and MBES techniques in deeper waters (Komatsu et al, 2003;Rattray et al, 2009;Ierodiaconou et al, 2011;McGonigle et al, 2011;;van Rein et al, 2011).…”

Section: Discussionmentioning

confidence: 75%

Predicting spatial kelp abundance in shallow coastal waters using the acoustic ground discrimination system RoxAnn

Mielck

Bartsch

Hass

et al. 2014

Estuarine, Coastal and Shelf Science

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a b s t r a c tKelp forests represent a major habitat type in coastal waters worldwide and their structure and distribution is predicted to change due to global warming. Despite their ecological and economical importance, there is still a lack of reliable spatial information on their abundance and distribution. In recent years, various hydroacoustic mapping techniques for sublittoral environments evolved. However, in turbid coastal waters, such as off the island of Helgoland (Germany, North Sea), the kelp vegetation is present in shallow water depths normally excluded from hydroacoustic surveys. In this study, single beam survey data consisting of the two seafloor parameters roughness and hardness were obtained with RoxAnn from water depth between 2 and 18 m. Our primary aim was to reliably detect the kelp forest habitat with different densities and distinguish it from other vegetated zones. Five habitat classes were identified using underwater-video and were applied for classification of acoustic signatures. Subsequently, spatial prediction maps were produced via two classification approaches: Linear discriminant analysis (LDA) and manual classification routine (MC). LDA was able to distinguish dense kelp forest from other habitats (i.e. mixed seaweed vegetation, sand, and barren bedrock), but no variances in kelp density. In contrast, MC also provided information on medium dense kelp distribution which is characterized by intermediate roughness and hardness values evoked by reduced kelp abundances. The prediction maps reach accordance levels of 62% (LDA) and 68% (MC). The presence of vegetation (kelp and mixed seaweed vegetation) was determined with higher prediction abilities of 75% (LDA) and 76% (MC). Since the different habitat classes reveal acoustic signatures that strongly overlap, the manual classification method was more appropriate for separating different kelp forest densities and low-lying vegetation. It became evident that the occurrence of kelp in this area is not simply linked to water depth. Moreover, this study shows that the two seafloor parameters collected with RoxAnn are suitable indicators for the discrimination of different densely vegetated seafloor habitats in shallow environments.

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Section: Discussionmentioning

confidence: 75%

Predicting spatial kelp abundance in shallow coastal waters using the acoustic ground discrimination system RoxAnn

Mielck

Bartsch

Hass

et al. 2014

Estuarine, Coastal and Shelf Science

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“…The greatest reduction in sound speed and increase attenuation in the seagrass bed occur in the uppermost layer of the sediment, where the biomass content is most significant. In contrast, the abundance of macroscopic plant biomass in the bare-sediment patch is negligible, taking on values less than 8 g/cm 2 . The bulk density is approximately 5% to 15% lower in the seagrass cores than in the bare sediment cores for the uppermost 10 cm layer.…”

Section: Resultsmentioning

confidence: 94%

“…An important acoustical effect is due to bubble production by the plants, which can have a significant impact on both object detection and bottom mapping sonars by increasing clutter through reflection, absorption, and scattering of sound. 1,2 Remote sensing techniques have been demonstrated to monitor biological markers, such as photosynthetic activity from seagrass, as an assessment of marine ecosystem health. 3,4 Additionally, seismo-acoustic survey tools have been investigated to obtain carbon sink estimates for the sediment underneath seagrass beds.…”

Section: Introductionmentioning

confidence: 99%

Sound speed and attenuation measurements within a seagrass meadow from the water column into the seabed

Lee¹,

Ballard²,

McNeese³

et al. 2017

The Journal of the Acoustical Society of America

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In situ measurements of sound speed and attenuation at 50 kHz were conducted in a Thalassia testudium meadow. Measurements were obtained at discrete depths in the water column, in the seagrass canopy, and in the sediment beneath the seagrass. Measurements were also obtained in bare sediment located a few meters away. Sediment biomass abundance was measured from cores collected at each site. Even though the measurements were obtained in the dormant season (winter), significant differences in sound speed and attenuation were observed in the sediment beneath the seagrass bed compared to the bare sediment.

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“…Acoustic surveys are also strong in detecting seagrass beds due to acoustic reflection of seagrass stronger than those of sand or mud (Komatsu et al, 2003 Table 2. Error matrix of supervised classification of five habitat classes in Akkeshi Lake.…”

Section: Wwwintechopencommentioning

confidence: 99%

“…Sagawa et al, 2008;2010). Narrow multi-beam sonar can measure three-dimensional spatial distribution of seagrass (Komatsu et al, 2003). However, usually, the boat equipped with these materials navigates at 3 to 4 knots (about 5 to 7 km/hr) with narrow swath width.…”

Section: Wwwintechopencommentioning

confidence: 99%

Mapping is a Key for Sustainable Development of Coastal Waters: Examples of Seagrass Beds and Aquaculture Facilities in Japan with Use of ALOS Images

Komatsu¹,

Sagawa²,

Sawayama³

et al. 2012

Sustainable Development - Education, Business and Management - Architecture and Building Construction - Agriculture and Food Se

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Use of multi-beam sonar to map seagrass beds in Otsuchi Bay on the Sanriku Coast of Japan

Cited by 87 publications

References 17 publications

Predicting spatial kelp abundance in shallow coastal waters using the acoustic ground discrimination system RoxAnn

Predicting spatial kelp abundance in shallow coastal waters using the acoustic ground discrimination system RoxAnn

Sound speed and attenuation measurements within a seagrass meadow from the water column into the seabed

Mapping is a Key for Sustainable Development of Coastal Waters: Examples of Seagrass Beds and Aquaculture Facilities in Japan with Use of ALOS Images

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